Prospective Follow-Up of Adolescents With and at Risk for Depression: Protocol and Methods of the Identifying Depression Early in Adolescence Risk Stratified Cohort Longitudinal Assessments.

Objective: To present the protocol and methods for the prospective longitudinal assessments-including clinical and digital phenotyping approaches-of the Identifying Depression Early in Adolescence Risk Stratified Cohort (IDEA-RiSCo) study, which comprises Brazilian adolescents stratified at baseline by risk of developing depression or presence of depression. Method: Of 7,720 screened adolescents aged 14 to 16 years, we recruited 150 participants (75 boys, 75 girls) based on a composite risk score: 50 with low risk for developing depression (LR), 50 with high risk for developing depression (HR), and 50 with an active untreated major depressive episode (MDD). Three annual follow-up assessments were conducted, involving clinical measures (parent- and adolescent-reported questionnaires and psychiatrist assessments), active and passive data sensing via smartphones, and neurobiological measures (neuroimaging and biological material samples). Retention rates were 96% (Wave 1), 94% (Wave 2), and 88% (Wave 3), with no significant differences by sex or group (p > .05). Participants highlighted their familiarity with the research team and assessment process as a motivator for sustained engagement. Discussion: This protocol relied on novel aspects, such as the use of a WhatsApp bot, which is particularly pertinent for low- to-middle-income countries, and the collection of information from diverse sources in a longitudinal design, encompassing clinical data, self-reports, parental reports, Global Positioning System (GPS) data, and ecological momentary assessments. The study engaged adolescents over an extensive period and demonstrated the feasibility of conducting a prospective follow-up study with a risk-enriched cohort of adolescents in a middle-income country, integrating mobile technology with traditional methodologies to enhance longitudinal data collection.

This article details the study protocol and methods used in the longitudinal assessment of 150 Brazilian teenagers with depression and at risk for depression as part of the Identifying Depression Early in Adolescence Risk Stratified Cohort (IDEA-RiSCo). Over 3 years, the authors collected clinical and digital data using innovative mobile technology, including a WhatsApp bot. Most adolescents participated in all the study phases, showing feasibility of prospective follow-up in a middle-income country. This approach allowed for a deeper understanding of depression in young populations, particularly in areas where mental health research is scarce.

Predicting functional impairment in euthymic patients with mood disorder: A 5-year follow-up.

Major Depressive Disorder and Bipolar Disorder are psychiatric disorders associated with psychosocial impairment. Despite clinical improvement, functional complaints usually remain, mainly impairing occupational and cognitive performance. The aim of this study was to use machine learning techniques to predict functional impairment in patients with mood disorders. For that, analyzes were performed using a population-based cohort study. Participants diagnosed with a mood disorder at baseline and reassessed were considered (n = 282). Random forest (RF) with previous recursive feature selection and LASSO algorithms were applied to a training set with imputed data by bagged trees resulting in two main models. Following recursive feature selection, 25 variables were retained. The RF model had the best performance compared to LASSO. The most important variables in predicting functional impairment were sexual abuse, severity of depressive, anxiety, and somatic symptoms, physical neglect, emotional abuse, and physical abuse. The model demonstrated acceptable performance to predict functional impairment. However, our sample is composed of young participants and the model may not generalize to older individuals with mood disorders. More studies are needed in this direction. The presented calculator has clinical, sociodemographic, and environmental data, demonstrating that it is possible to use such information to predict functional performance.

Patterns of high-risk drinking among medical students: A web-based survey with machine learning.

BACKGROUND: Prior studies have found increased rates of alcohol consumption among physicians and medical students. The present study aims to build machine learning (ML) models to identify patterns of high-risk drinking (HRD), including alcohol use disorder, within this population. METHODS: We analyzed data collected through a web-based survey among Brazilian medical students. Variables included sociodemographic data, personal information, university status, and mental health. Stratification for HRD was carried out based on the AUDIT-C scores. Three ML algorithms were used to build classifiers to predict HRD among medical students: elastic net regularization, random forest, and artificial neural networks. Model interpretation techniques were adopted to assess the most influential predictors for models' decisions, which represent potential factors associated with HRD. RESULTS: A total of 4840 medical students were included in the study. The prevalence of HRD was 53.03%. The three ML models built were able to distinguish individuals with HRD from low-risk drinking (LRD) with very similar performance. The average AUC scores in the cross-validation procedure were around 0.72, and this performance was replicated in the test set. The most important features for the ML models were the use of tobacco and cannabis, monthly family income, marital status, sexual orientation, and physical activities. CONCLUSIONS: This study proposes that ML models may serve as tools for initial screening of students regarding their susceptibility for at-risk drinking or alcohol use disorder. In addition, we identified several key factors associated with HRD that could be further investigated and explored for preventive and assistance measures.

Loneliness, but not social distancing, is associated with the incidence of suicidal ideation during the COVID-19 outbreak: a longitudinal study.

BACKGROUND: Although social distancing is necessary to decrease COVID-19 dissemination, it might also be associated with suicidal ideation. Therefore, we analyzed the impact of social distancing and loneliness in suicidal ideation. METHODS: We performed two waves of a snowball sample, web-based survey in Brazil (W1: from May 6th to June 6th, 2020; W2: from June 6th to July 6th, 2020). We assessed whether risk factors related to social relationships (loneliness, living alone, not leaving home, and the number of days practicing social distancing) at W1 were associated with suicidal ideation at W1 and W2 using multiple regression models. Analyses were adjusted for sociodemographic, mental health, and lifestyle variables. RESULTS: A total of 1,674 (18-75 years old; 86.5% females) were included in our longitudinal sample. Living alone (OR: 1.16; 95%CI = 1.03 - 1.30; p=0.015), number of days practicing social distancing (OR: 1.002; 95%CI = 1.000 - 1.004; p=0.027), and loneliness (OR: 1.49; 95%CI = 1.32 - 1.68; p<0.001) were associated with suicidal ideation in the cross-sectional analysis of W1. Only loneliness (OR= 2.12; 95%CI = 1.06 - 4.24; p = 0.033) remained significant as a risk factor to suicidal ideation in the longitudinal analysis between both waves. LIMITATION: Snowball, convenience sample design limits outcome estimates. Assessments were not objectively performed. CONCLUSION: Loneliness was consistently associated with the incidence of suicidal ideation, while other variables, such as living alone, not leaving home, and the number of days practicing social distancing, were not. Measures to overcome loneliness are therefore necessary to reduce suicidal ideation during pandemics.

Risk factors for suicidality in patients with panic disorder: A systematic review and meta-analysis.

BACKGROUND: Subjects with panic disorder are nearly 4 times as likely to attempt suicide as compared to subjects without this condition. METHODS: We searched the literature from Jan 1, 1960 to May, 4, 2019. Articles that reported a dichotomous sample of patients with panic disorder with and without suicidal behavior were included. OUTCOMES: Twelve studies with 1958 participants were included. Comorbid depression (k = 3, ES = 4.47 [2.63; 7.60]), depressive symptoms (k = 2, ES = 1.98 [1.26; 3.11]), older age (k = 3, ES = 1.66 [1.32; 2.10]), younger age of panic disorder onset (k = 2, ES = 0.65 [0.45; 0.94]), and history of alcohol dependence (k = 2, ES = 8.70 [1.20; 63.04]) were associated with suicide attempt in panic disorder. Depressive symptoms (k = 2, ES = 2.29 (1.60; 3.37]), anxiety symptoms (k = 2, ES = 1.90 [1.33; 2.69]), longer illness duration (k = 2, ES = 3.31 [1.90; 5.74]), comorbid depressive disorder (k = 4, ES = 3.88 [2.03; 7.41]), agoraphobia (k = 2, ES = 4.60 [1.47; 14.42]) and younger age of onset (k = 2, ES = 0.60 [0.38; 0.96]) were associated with suicidal ideation in panic disorder. INTERPRETATION: Our findings provide a framework for the development of suicide prevention strategies in this population.

Revisiting the Werther Effect in the 21st Century: Bullying and Suicidality Among Adolescents Who Watched 13 Reasons Why.

Unlike most leading causes of death in the United States, suicide rates have not declined during the past 50 years.1 Among young people the situation is even more dramatic, because suicide rates are rising,2 and suicide is now the second cause of death in 15- to 29-year-olds globally.3 It has been suggested that descriptions of suicide in the media might affect behavior and that the young might be more vulnerable to this effect.4.